Things to keep in mind while talking to customers

Listening

There is a difference between ‘listening’ and ‘active listening’ and you need to be focusing on the latter. ‘Active listening’ is about paying close attention to what’s being said without distractions and comprehending what the customer needs help with and what more they might need help with. Don’t be afraid to ask questions and clarify, this is much better than making assumptions. Be patient and only speak when you have to or when you know what you’re talking about.

Tone

Maintaining a consistent, friendly, but professional tone is crucial. Questions like “Anything else?” and “What else can I help you with?” may ask the same thing, but their tones are worlds apart. The right tone can make interactions feel more personal and less scripted.

Positive language

Transform the conversation from a 'you' vs 'us' to a collaborative 'we'. Avoid phrases like "You need to" and instead use inclusive language that shows empathy and understanding. When a customer says “I cannot access my account”, instead of saying “Try resetting your password” say something like “That must be frustrating, let’s troubleshoot this together.”

Brevity and warmth

Customers are likely to ignore lengthy replies. Keep sentences and paragraphs short, and use screenshots, gifs, and links for concise yet informative responses. The goal is to answer their queries while making them feel heard but at the same time be respectful of their time and not expect them to spend more than a few minutes reading and understanding your answer.

Customizing AI Agent Assist for diverse customer needs

AI Agent Assist tools are growing in prominence and companies realize how important it is to empower your agents which also equates to happier customers and eventually business growth. Customers want to be seen and heard as individuals, and not just another ticket number. AI Agent Assist is an efficient way of crafting personalized support for agents, keeping in mind important factors like their tone, voice, and sentiment. It's about offering responses that resonate on a logical level, with empathy to forge a stronger bond. What’s more, agents can further edit these responses to make them more personalized to their voice so that customers don’t feel like they’re talking to a bot.

Threado AI understands this and focuses on providing agents with a tool that understands customer concerns and can be customized by the agent to auto-suggest answers accordingly.

How can Threado AI Agent Assist be customized?

Threado AI Agent Assist works in two ways - as a Chrome extension where the bot can be launched on top of tabs and on Slack channels where the bot automatically responds to queries. You can customize the bot to match the brand voice or tone and based on these customizations, the bot suggests and personalizes responses based on that.

Signup to Threado AI (if you haven’t already)

Then, you can setup Threado AI Agent Assist by first training the bot and then testing it.

Now, you can customize the bot for the Chrome app and Slack.

Chrome App

Customization options include defining the voice and tone so that the AI can apply these checks automatically to standardize and improve agent responses when they want the bot to auto-suggest responses.

You can define the AI check and improvements for 2 primary features -

• Auto-improving - The bot can improve an agents response to customers by making it more professional, friendly, casual, and empathetic. So, essentially, the agent can write a direct answer and the bot can improve it based on the customizations selected.
• Response checks - The not can check if the agent’s response includes a greeting, has enough clarity, or is grammatically or logically correct or not. An agent can write and answer and get it checked by the bot to make sure it is precise, clear, and correct.

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Personalization is Key

Personalizing customer support is crucial for making customers feel important and understood. By analyzing customer data, companies can learn about individual preferences and behaviors, leading to support that feels specially designed for each customer.

This approach involves recognizing each customer's unique needs based on their history and preferences, allowing for more precise and relevant help. For example, suggesting products or services that match what they've liked before can significantly improve their experience.

Furthermore, by using AI and machine learning, companies can anticipate and address potential issues before they become problems, showing customers that their needs are understood and valued.

Ultimately, this personalized service fosters stronger relationships with customers. When people feel a company truly gets them, they're more likely to stick around and keep coming back